Python Machine Learning Model Production by Django & Heroku

[mitsumi]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 8 lectures (53m) | Size: 268 MB

Transform your model into an API service, learn to become a full stack data scientist

What you'll learn:
Python Machine learning model production with Django
Using Django to create RESTful API endpoint to service a Machine learning model
Building a real machine learning model, and deploy it into production
Basic Django skills
Sentiment analysis model
Python machine learning
Scikit-learn
Basic Natural Language Processing: converting textual data into word-frequency matrix

Requirements
Basic understanding of Python and Django
A Mac or a Linux / Unix based computer

Description
Have you had to beg your manager or engineer teammates to reprioritize their work to help you launch your fantastic machine learning model?

But they said no.

Have you ever wondered how a modern recommendation engine push recommended movies for your personal experience?

You are in the right place.

This 1-hour course gives all you need to create an end-to-end machine learning modeling pipeline.

And you will never need to rely on others to help you productionizing your model again.

It will significantly enhance your job security in this challenging time .

You will learn the following, step by step

Set up your local development environment.

Train a sentimental prediction model using movie review data with Scikit-learn .

Fine-tune the parameters and benchmark the results.

Convert textual data into a word frequency matrix.

Persist the model that performs the best, create a RESTful API endpoint with Django and deploy it into a productionized environment onto Heroku.

Test the API from your browser and curl .

After deploying it to the cloud, your model can serve the whole internet.

You can use the same framework to serve other models.

Other people and teams can easily consume your model results by making API calls.

Significantly improve your productivity and job security as a data scientist.

Prerequisites

An Apple computer (aka a MAC) or a UNIX / Linux based machine, as everything is recorded on a MAC, if you are using a Windows machine, you may not get the most benefit from this tutorial;

A basic understanding of Python and machine learning will use scikit-learn, a python library, to build a classification model.

About the author

Most recently, Leon was a senior manager of applied machine learning at  Apple .

He led data scientists, software engineers, and product managers in building large-scale machine learning systems for Apple's billion-dollar businesses.

Before that, he was head of data science at Chegg and a research scientist at Amazon , where he developed Amazon's real-time pricing engine for millions of products sold there.

Who this course is for
Data scientists who want to become full stack
Data scientists who want to become more productive
Python beginners who wants to build a machine learning model

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